X-ray CT reconstruction via l0 gradient projection

Using a small number of sampling views during a CT (computed tomography) exam is a widely accepted technique for lowdose CT reconstruction, which reduces the risk of inducing cancer or other diseases in patients. In this scenario, total variation (TV) based compressed sensing (CS) methods, which uses a regularization term that penalizes the l(1) norm of the reconstructed image's gradient, outperform the traditional FBP (filtered back-projection) based algorithms in CT reconstruction. Furthermore, in order to reduce well-known artifacts (smoothed edges and texture details) favored by TV-based CS methods, several variants have been proposed, which, in a general context, can be understood as using a regularization term that approximates the l(0) norm of the reconstructed image's gradient. These type of methods yield state-of-theart reconstruction results. In this paper we exploit a variant of the l(0) gradient minimization problem, which directly penalizes the number of non-zero gradients in the reconstructed image, and propose to solve the low-dose CT reconstruction problem. Extended experiments, based on the ASTRA toolbox, show that the propose method is faster (almost twice as fast) and delivers higher quality reconstructions than TV-based CS methods and alternatives that reduce smooth artifacts.